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Found 12 papers, 10 papers with code
Improving Relation Extraction by Pre-trained Language Representations
Unlike previous relation extraction models, TRE uses pre-trained deep language representations instead of explicit linguistic features to inform the relation classification and combines it with the self-attentive Transformer architecture to effectively model long-range dependencies between entity mentions.
Ranked #24 on
Relation Extraction
on SemEval-2010 Task-8
Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction
Distantly supervised relation extraction is widely used to extract relational facts from text, but suffers from noisy labels.
TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task
TACRED (Zhang et al., 2017) is one of the largest, most widely used crowdsourced datasets in Relation Extraction (RE).
Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction
Despite the recent progress, little is known about the features captured by state-of-the-art neural relation extraction (RE) models.
Layerwise Relevance Visualization in Convolutional Text Graph Classifiers
Representations in the hidden layers of Deep Neural Networks (DNN) are often hard to interpret since it is difficult to project them into an interpretable domain.
A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition
Pre-trained language models (PLM) are effective components of few-shot named entity recognition (NER) approaches when augmented with continued pre-training on task-specific out-of-domain data or fine-tuning on in-domain data.
Contrastive Learning
Low Resource Named Entity Recognition
+4
Considering Likelihood in NLP Classification Explanations with Occlusion and Language Modeling
Recently, state-of-the-art NLP models gained an increasing syntactic and semantic understanding of language, and explanation methods are crucial to understand their decisions.
Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction
Definition Extraction systems are a valuable knowledge source for both humans and algorithms.
Learning Explanations from Language Data
PatternAttribution is a recent method, introduced in the vision domain, that explains classifications of deep neural networks.
Why only Micro-F1? Class Weighting of Measures for Relation Classification
Relation classification models are conventionally evaluated using only a single measure, e. g., micro-F1, macro-F1 or AUC.
Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning
Named Entity Recognition (NER) in domains like e-commerce is an understudied problem due to the lack of annotated datasets.
A Medical Information Extraction Workbench to Process German Clinical Text
However, in the context of clinical text processing the number of accessible datasets is scarce -- and so is the number of existing tools.
